{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:XJGAZGC3MYS3LJQJKIEHMA3JE5","short_pith_number":"pith:XJGAZGC3","canonical_record":{"source":{"id":"2605.12540","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2026-05-08T03:42:02Z","cross_cats_sorted":[],"title_canon_sha256":"a6274fcb7f87d3cb8ea2070d8eb21f9201d8a252fcb8e654c7f7412b5e0bb677","abstract_canon_sha256":"453c5d0078dc9bfd560f4de0a1986e7d6c51345ade9a809e81efa097fb8a3639"},"schema_version":"1.0"},"canonical_sha256":"ba4c0c985b6625b5a60952087603692762aac2fe51fcc89b7171b5cbd48915aa","source":{"kind":"arxiv","id":"2605.12540","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12540","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12540v1","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12540","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"pith_short_12","alias_value":"XJGAZGC3MYS3","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"XJGAZGC3MYS3LJQJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"XJGAZGC3","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:XJGAZGC3MYS3LJQJKIEHMA3JE5","target":"record","payload":{"canonical_record":{"source":{"id":"2605.12540","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2026-05-08T03:42:02Z","cross_cats_sorted":[],"title_canon_sha256":"a6274fcb7f87d3cb8ea2070d8eb21f9201d8a252fcb8e654c7f7412b5e0bb677","abstract_canon_sha256":"453c5d0078dc9bfd560f4de0a1986e7d6c51345ade9a809e81efa097fb8a3639"},"schema_version":"1.0"},"canonical_sha256":"ba4c0c985b6625b5a60952087603692762aac2fe51fcc89b7171b5cbd48915aa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:10:02.358402Z","signature_b64":"rUoIsWxy8ztzMFv/Q63Kn+YNYniyAI4sDuVGVVyvUK/Cse4FJ+H4ufKATX+Oz8EGHXuVNdc66j6wJ6tYDGgYDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba4c0c985b6625b5a60952087603692762aac2fe51fcc89b7171b5cbd48915aa","last_reissued_at":"2026-05-18T03:10:02.357933Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:10:02.357933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.12540","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:10:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DwkKdnHHY0xC+L4NHwhhQp/DzCGcg8diwLFNv+UX0oawIrSnqcSvoQhdWboPS+f4lx438Q5LceE2TRsFf2OjAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:21:21.918197Z"},"content_sha256":"2412591b51e718b4f42006302e58ec12b1d9a10321791f5b4aecbf285713a5f6","schema_version":"1.0","event_id":"sha256:2412591b51e718b4f42006302e58ec12b1d9a10321791f5b4aecbf285713a5f6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:XJGAZGC3MYS3LJQJKIEHMA3JE5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stochastic Smoothed Particle Hydrodynamics for Stochastic Mechanics Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Stochastic SPH represents uncertainties via polynomial chaos and Karhunen-Loève expansions to turn stochastic PDEs into coupled deterministic ODEs solved on particles.","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Md Rushdie Ibne Islam, Mridul Tiwari, Sawan Kumar, Souvik Chakraborty","submitted_at":"2026-05-08T03:42:02Z","abstract_excerpt":"Smoothed Particle Hydrodynamics (SPH_ is a mesh-free Lagrangian method renowned for modeling large deformations and free-surface flows, yet classical formulations remain confined to deterministic systems. We introduce Stochastic SPH (S-SPH), which employs orthogonal Polynomial Chaos expansions to represent uncertainties in system parameters, forcing functions, and boundary or initial conditions, while spatial variation is captured via the SPH kernel. Random fields are discretized through Karhunen-Lo\\`eve expansions, and a Galerkin projection in the polynomial basis transforms the underlying SP"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"S-SPH achieves up to three orders of magnitude reduction in computational cost relative to direct sampling approaches while demonstrating excellent agreement with Monte Carlo simulation statistics of mean and variance on benchmark problems.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the chosen polynomial chaos order and Karhunen-Loève truncation are sufficient to capture the statistics of the target uncertainties without significant truncation error, and that the ghost-particle boundary treatment remains stable and accurate once the stochastic coefficients are introduced.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"S-SPH extends mesh-free SPH to stochastic problems via polynomial chaos and KL expansions, delivering mean and variance statistics that match Monte Carlo at up to 1000 times lower cost on benchmark advection and Burgers flows.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Stochastic SPH represents uncertainties via polynomial chaos and Karhunen-Loève expansions to turn stochastic PDEs into coupled deterministic ODEs solved on particles.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e94d6c8955051554d35ca927821a1263fff9670f1e32519768ca8a4aba3dfbe4"},"source":{"id":"2605.12540","kind":"arxiv","version":1},"verdict":{"id":"8f8439b4-c9a2-4a44-97c6-3b010c4c9e13","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T21:34:55.358758Z","strongest_claim":"S-SPH achieves up to three orders of magnitude reduction in computational cost relative to direct sampling approaches while demonstrating excellent agreement with Monte Carlo simulation statistics of mean and variance on benchmark problems.","one_line_summary":"S-SPH extends mesh-free SPH to stochastic problems via polynomial chaos and KL expansions, delivering mean and variance statistics that match Monte Carlo at up to 1000 times lower cost on benchmark advection and Burgers flows.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the chosen polynomial chaos order and Karhunen-Loève truncation are sufficient to capture the statistics of the target uncertainties without significant truncation error, and that the ghost-particle boundary treatment remains stable and accurate once the stochastic coefficients are introduced.","pith_extraction_headline":"Stochastic SPH represents uncertainties via polynomial chaos and Karhunen-Loève expansions to turn stochastic PDEs into coupled deterministic ODEs solved on particles."},"references":{"count":55,"sample":[{"doi":"","year":2024,"title":"Sph-exa2: Scaling smoothed particle hydrodynamics to exascale for cosmology and astrophysics, 2024","work_id":"16b57704-26fa-44f9-98a6-6824e0343bf2","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Smoothed particle magnetohydrodynamics: Applications to astrophysical problems.Frontiers in Astronomy and Space Sciences, 10, 2023","work_id":"c76c2aa1-01da-4608-9e64-97923cf54c90","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2010,"title":"V . Springel. E pur si muove: Galilean-invariant cosmological hydrodynamical simulation.Monthly Notices of the Royal Astronomical Society, 401(2):791–851, 2010","work_id":"bd36e1c1-7fcd-40b2-8f9c-7e40eaa81956","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2012,"title":"Violeau.Smoothed Particle Hydrodynamics: A Meshfree Particle Method","work_id":"91efc729-1c0c-4e0f-aa55-97251fc08303","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2008,"title":"Y . Ono, S. Nishida, and J. Kiyono. Sph simulation for seismic behavior of earth structures. In14th World Conference on Earthquake Engineering, 2008","work_id":"bf744238-705a-47e9-8e63-2ba65891626f","ref_index":6,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":55,"snapshot_sha256":"a03de737395ec1ab04f35c7ef012f68da89b837cf8b711f9301698664567978f","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"8f8439b4-c9a2-4a44-97c6-3b010c4c9e13"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:10:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wUdjTXmxwSso6WEzDa3tB154/Y5uLh3QvFGDzTh26qgehLwXpVdaSEnhzDWuxUnHTbHV27V8fY2pl5FrCLj/CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:21:21.919391Z"},"content_sha256":"6e07dddff041b778f3f908d5588645ff5cd93eb7b3d17ec35123224838c85b00","schema_version":"1.0","event_id":"sha256:6e07dddff041b778f3f908d5588645ff5cd93eb7b3d17ec35123224838c85b00"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XJGAZGC3MYS3LJQJKIEHMA3JE5/bundle.json","state_url":"https://pith.science/pith/XJGAZGC3MYS3LJQJKIEHMA3JE5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XJGAZGC3MYS3LJQJKIEHMA3JE5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T21:21:21Z","links":{"resolver":"https://pith.science/pith/XJGAZGC3MYS3LJQJKIEHMA3JE5","bundle":"https://pith.science/pith/XJGAZGC3MYS3LJQJKIEHMA3JE5/bundle.json","state":"https://pith.science/pith/XJGAZGC3MYS3LJQJKIEHMA3JE5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XJGAZGC3MYS3LJQJKIEHMA3JE5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XJGAZGC3MYS3LJQJKIEHMA3JE5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"453c5d0078dc9bfd560f4de0a1986e7d6c51345ade9a809e81efa097fb8a3639","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2026-05-08T03:42:02Z","title_canon_sha256":"a6274fcb7f87d3cb8ea2070d8eb21f9201d8a252fcb8e654c7f7412b5e0bb677"},"schema_version":"1.0","source":{"id":"2605.12540","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12540","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12540v1","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12540","created_at":"2026-05-18T03:10:02Z"},{"alias_kind":"pith_short_12","alias_value":"XJGAZGC3MYS3","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"XJGAZGC3MYS3LJQJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"XJGAZGC3","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:6e07dddff041b778f3f908d5588645ff5cd93eb7b3d17ec35123224838c85b00","target":"graph","created_at":"2026-05-18T03:10:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"S-SPH achieves up to three orders of magnitude reduction in computational cost relative to direct sampling approaches while demonstrating excellent agreement with Monte Carlo simulation statistics of mean and variance on benchmark problems."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the chosen polynomial chaos order and Karhunen-Loève truncation are sufficient to capture the statistics of the target uncertainties without significant truncation error, and that the ghost-particle boundary treatment remains stable and accurate once the stochastic coefficients are introduced."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"S-SPH extends mesh-free SPH to stochastic problems via polynomial chaos and KL expansions, delivering mean and variance statistics that match Monte Carlo at up to 1000 times lower cost on benchmark advection and Burgers flows."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Stochastic SPH represents uncertainties via polynomial chaos and Karhunen-Loève expansions to turn stochastic PDEs into coupled deterministic ODEs solved on particles."}],"snapshot_sha256":"e94d6c8955051554d35ca927821a1263fff9670f1e32519768ca8a4aba3dfbe4"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Smoothed Particle Hydrodynamics (SPH_ is a mesh-free Lagrangian method renowned for modeling large deformations and free-surface flows, yet classical formulations remain confined to deterministic systems. We introduce Stochastic SPH (S-SPH), which employs orthogonal Polynomial Chaos expansions to represent uncertainties in system parameters, forcing functions, and boundary or initial conditions, while spatial variation is captured via the SPH kernel. Random fields are discretized through Karhunen-Lo\\`eve expansions, and a Galerkin projection in the polynomial basis transforms the underlying SP","authors_text":"Md Rushdie Ibne Islam, Mridul Tiwari, Sawan Kumar, Souvik Chakraborty","cross_cats":[],"headline":"Stochastic SPH represents uncertainties via polynomial chaos and Karhunen-Loève expansions to turn stochastic PDEs into coupled deterministic ODEs solved on particles.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2026-05-08T03:42:02Z","title":"Stochastic Smoothed Particle Hydrodynamics for Stochastic Mechanics Problems"},"references":{"count":55,"internal_anchors":0,"resolved_work":55,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Sph-exa2: Scaling smoothed particle hydrodynamics to exascale for cosmology and astrophysics, 2024","work_id":"16b57704-26fa-44f9-98a6-6824e0343bf2","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Smoothed particle magnetohydrodynamics: Applications to astrophysical problems.Frontiers in Astronomy and Space Sciences, 10, 2023","work_id":"c76c2aa1-01da-4608-9e64-97923cf54c90","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"V . Springel. E pur si muove: Galilean-invariant cosmological hydrodynamical simulation.Monthly Notices of the Royal Astronomical Society, 401(2):791–851, 2010","work_id":"bd36e1c1-7fcd-40b2-8f9c-7e40eaa81956","year":2010},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Violeau.Smoothed Particle Hydrodynamics: A Meshfree Particle Method","work_id":"91efc729-1c0c-4e0f-aa55-97251fc08303","year":2012},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":6,"title":"Y . Ono, S. Nishida, and J. Kiyono. Sph simulation for seismic behavior of earth structures. In14th World Conference on Earthquake Engineering, 2008","work_id":"bf744238-705a-47e9-8e63-2ba65891626f","year":2008}],"snapshot_sha256":"a03de737395ec1ab04f35c7ef012f68da89b837cf8b711f9301698664567978f"},"source":{"id":"2605.12540","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T21:34:55.358758Z","id":"8f8439b4-c9a2-4a44-97c6-3b010c4c9e13","model_set":{"reader":"grok-4.3"},"one_line_summary":"S-SPH extends mesh-free SPH to stochastic problems via polynomial chaos and KL expansions, delivering mean and variance statistics that match Monte Carlo at up to 1000 times lower cost on benchmark advection and Burgers flows.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Stochastic SPH represents uncertainties via polynomial chaos and Karhunen-Loève expansions to turn stochastic PDEs into coupled deterministic ODEs solved on particles.","strongest_claim":"S-SPH achieves up to three orders of magnitude reduction in computational cost relative to direct sampling approaches while demonstrating excellent agreement with Monte Carlo simulation statistics of mean and variance on benchmark problems.","weakest_assumption":"That the chosen polynomial chaos order and Karhunen-Loève truncation are sufficient to capture the statistics of the target uncertainties without significant truncation error, and that the ghost-particle boundary treatment remains stable and accurate once the stochastic coefficients are introduced."}},"verdict_id":"8f8439b4-c9a2-4a44-97c6-3b010c4c9e13"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2412591b51e718b4f42006302e58ec12b1d9a10321791f5b4aecbf285713a5f6","target":"record","created_at":"2026-05-18T03:10:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"453c5d0078dc9bfd560f4de0a1986e7d6c51345ade9a809e81efa097fb8a3639","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2026-05-08T03:42:02Z","title_canon_sha256":"a6274fcb7f87d3cb8ea2070d8eb21f9201d8a252fcb8e654c7f7412b5e0bb677"},"schema_version":"1.0","source":{"id":"2605.12540","kind":"arxiv","version":1}},"canonical_sha256":"ba4c0c985b6625b5a60952087603692762aac2fe51fcc89b7171b5cbd48915aa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ba4c0c985b6625b5a60952087603692762aac2fe51fcc89b7171b5cbd48915aa","first_computed_at":"2026-05-18T03:10:02.357933Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:10:02.357933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rUoIsWxy8ztzMFv/Q63Kn+YNYniyAI4sDuVGVVyvUK/Cse4FJ+H4ufKATX+Oz8EGHXuVNdc66j6wJ6tYDGgYDw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:10:02.358402Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12540","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2412591b51e718b4f42006302e58ec12b1d9a10321791f5b4aecbf285713a5f6","sha256:6e07dddff041b778f3f908d5588645ff5cd93eb7b3d17ec35123224838c85b00"],"state_sha256":"39d14eabf094d0432232f6c7c85f85c11eda1fbaf03928ab03b709a1aece9a92"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IuIyEhxn/BvKu21HT38ad7zHAMimib93GCeYyzj0r0YlUVuwnv32egdNunBFuVevbgA05MUseEIw8X7lX3WkAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:21:21.924501Z","bundle_sha256":"a7cd40180c0beefeb39bae63fdc536cba99998d957785992b7d77b9205fe08a1"}}